S116: Using highly-efficient nonlinear experimental design methods for modeling and optimizing fermentation processes

Optimization of fermentation processes is a difficult task due to the potential for high dimensionality and non-linearity. Here we present two novel and highly efficient methods for experimental fermentation optimization. The first approach is based on using a truncated genetic algorithm with a developing neural network model to choose the best experiments to run. The second approach uses information theory, along with Bayesian regularized neural network models, for experiment selection. Both of these methods seek to capture as much information as possible from the developing knowledge base during process development.  To evaluate these methods experimentally, we used them to develop a new chemically-defined medium for Lactococcus lactis IL1403, along with an optimal temperature and initial pH, to achieve maximum cell growth. The media consisted of 19 defined components or groups of components. The optimization results show that the maximum cell growth from the optimal process of each novel method is generally comparable to or higher than that achieved using a traditional statistical experimental design method, but these optima are reached in about half of the experiments (73 to 94 vs. 161, depending on the method used). The optimal chemically-defined media identified are rich media that can support high cell density growth 3.5 to 4 times higher than the best reported synthetic medium (SA) and 72% higher than a commonly-used complex medium (M17) at optimization and larger scales.  The resulting models from any of these methods can also be useful tools in process characterization prior to regulatory approval and manufacturing.